VAKCINA

Chinese President Xi Jinping pledged 2 billion doses of COVID-19 vaccines would be supplied to the world through this year, increasing China’s commitment as the largest exporter of the shots. https://t.co/j9JEkb5bP0

— The Associated Press (@AP) August 6, 2021

According to data from Israel’s Health Ministry which was published in a report by Channel 12 Sunday evening, fourteen people who have received a third dose of the Pfizer-BioNTech COVID vaccine were diagnosed with the coronavirus at least seven days after receiving the third dose.
Israel officially kicked off its mass vaccination campaign for the third dose on July 30th, and has vaccinated some 422,000 people with the third dose thus far. The third dose is available to members of high-risk groups, including the elderly.
Of the 14 recipients of the third dose who tested positive for the virus at least seven days after receiving the booster shot, eleven are over the age of 60, with the remaining three being younger people who received the shot because they suffer from compromised immune systems.
Two of the 14 people who were diagnosed with COVID at least a week after receiving the booster shot have been hospitalized as a result of their COVID infections. Sunday’s report did not specify whether the two are in serious condition.

Prošle nedelje u bolnici u Batajnici od 66 pacijenata 5 bilo vakcinisano.

Correlates of protection against infection from antibody neutralization assays vs efficacy

Best: Moderna, Pfizer/BioNTech, Novavax, Sputnik V
Next: antibodies from getting sick and recovering
Then: Covaxin (projected)
Then: J&J and AstraZeneca/Covishield
Lowest: CoronaVac pic.twitter.com/rHeDHTbrv6

— A.M. (@bhalomanush) May 17, 2021

Predicted efficacy against any variant based on antibody neutralization assays plugging in efficacy vs. W.T. (lineage detected in Wuhan).

Superb paper. https://t.co/Bfm610heEZ pic.twitter.com/g9hJd61oou

— A.M. (@bhalomanush) May 17, 2021

mRNA-1273 = Moderna
NVX... = Novavax
BNT162b2 = Pfizer/BioNTech
rAd26-s + ... = Sputnjik
Convalescent = preležali
Covaxin = Bharat Biotech (indijska s inaktiviranim virusom)
Ad26... = Johnson & Johnson tj. Janssen (jednodozna)
ChAdOx1... = Oxford/AstraZeneca
CoronaVac = Sinovac

a, Relationship between neutralization level and protection from SARS-CoV-2 infection. The reported mean neutralization level from phase 1 and 2 trials and the protective efficacy from phase 3 trials for seven vaccines, as well as the protection observed in a seropositive convalescent cohort, are shown (details of data sources are given in Supplementary Tables 1 and 2). The 95% CIs are indicated as vertical and as horizontal whiskers. The red solid line indicates the best fit of the logistic model and the red shading indicates the 95% predictive interval of the model. The mean neutralization level and protective efficacy of the Covaxin vaccine are indicated as a green circle (data from this study were available only after modeling was complete and did not contribute to fitting).

b, Schematic illustration of the logistic approach to identifying the protective neutralization level. The data for each study include the distribution of the measured in vitro neutralization titer against SARS-CoV-2 in vaccinated or convalescent subjects (as a proportion of the mean titer in convalescent subjects (dashed line)) (blue/red bell curve), accompanied by a level of protective efficacy for the same regimen. The efficacy is illustrated by the proportions of the bell curve ‘protected’ (blue) and ‘susceptible’ (red) for individual studies. The modeling fits the optimal 50% protective neutralization level (blue solid line, the shaded area indicates the 95% CI) that best estimates the correct levels of protection observed across the different studies.

c, Predictions of the leave-one-out analysis. Modeling was repeated multiple times using all potential sets of the seven vaccination studies and the convalescent study to predict the efficacy of the eighth study. The diagonal dashed line indicates the position of a 1:1 correlation (i.e., the relationship if the model were completely accurate). The horizontal whiskers indicate 95% CIs and the vertical whiskers indicate 95% predictive intervals.



a, Prediction of the effects of declining neutralization titer. Assuming that the observed relationship between neutralization level and protection is consistent over time, we estimate the decline in efficacy for vaccines with different levels of initial efficacy. The model assumes a half-life of the neutralization titer of 108 d over the first 250 d (as observed in a convalescent cohort5).

b, Modeling of the time for efficacy to drop to 70% (red line) or 50% (blue line) for scenarios with different initial efficacy. For example, for a group starting with an initial protective efficacy of 90%, the model predicts that 70% efficacy will be reached after 201 d and 50% efficacy will not be reached before 250 d.

c, Estimation of the impact of viral antigenic variation on vaccine efficacy. In vitro studies have shown that neutralization titers against some SARS-CoV-2 variants are reduced compared with titers against wild-type virus. If the relationship between neutralization and protection remains constant, we can predict the difference in protective efficacy against wild-type and variant viruses from the difference in neutralization level. The dashed line indicates equal protection against wild-type and variant strains. Details of the data and modeling are provided in the Methods.



a, The predicted relationship between efficacy against any symptomatic SARS-CoV-2 infection and the efficacy against severe infection. The black line indicates the best fit model for the relationship between protection against any versus severe SARS-CoV-2 infection. The shaded areas indicate the 95% CIs. Efficacy against severe infection was calculated using a threshold that was 0.15 times lower than that for mild infection (95% CI = 0.036–0.65) (see Methods and Supplementary Table 5).

b, Extrapolation of the decay of neutralization titers over time. This model uses the estimated half-life of SARS-CoV-2 neutralization titer in convalescent subjects of 108 d over the first 250 d5, after which the decay decreases linearly until it achieves a 10-year half-life (consistent with the long-term stability of antibody responses seen after other vaccines47,48). We simulate three scenarios, with decay of neutralization taking 1 year (blue dashed line), 1.5 years (purple dashed line) or 2 years (red dashed line) to slow to a 10-year half-life. For different initial starting levels the model projects the decay in neutralization titer over the subsequent 1,000 d (the gray shaded area indicates projections beyond the currently available data). The purple shaded region indicates being below the 50% protective titer for any symptomatic SARS-CoV-2 infection, and the orange shaded region indicates being below the 50% protective titer for severe SARS-CoV-2 infection. The model illustrates that, depending on the initial neutralization level, individuals may maintain protection from severe infection while becoming susceptible to mild infection (that is, with neutralization levels remaining in the purple shaded region).

c, Extrapolation of the trajectory of protection for groups with different starting levels of protection. The model uses the same assumptions for the rate of immune decay as discussed in b. The projections beyond 250 d (gray shaded region) rely on an assumption of how the decay in SARS-CoV-2 neutralization titer will slow over time. In addition, the modeling projects only how decay in neutralization is predicted to affect protection. Other mechanisms of immune protection may play important roles in providing long-term protection that are not captured in this simulation.

Supplementary Figure 3. Schematic based on the model of Khoury et al. illustrating the relationship between measured neutralising antibody titres (NAbT) against SARS-CoV-2 and observed real-world vaccine efficacy (VE). When NAbTs begin at a high level (e.g. against variants with spike proteins similar to the Wild-type spike in first-generation vaccines), small changes in NAbsTs have a small effect on VE. However, when titres begin from a lower level (for example due to reduced activity against VOCs such as B.1.617.2 ‘B.1.617.2’), small subsequent changes in NAbTs that would not have greatly affected VE previously now have a larger effect on VE.

The most vaccine-hesitant group of all? PhDs

A new study found that the most educated are the least likely to get jabbed

Crazy—police are investigating an anti-vax nurse is who may have injected 8600 people with saline when they were supposed to get the #COVID19 vaccine. Authorities have alerted those in Northern Germany who are potentially affected. https://t.co/3b3nksF8RE

— Eric Feigl-Ding (@DrEricDing) August 11, 2021

3) “The motive of the nurse, who was not named, was not clear but she had aired sceptical views about vaccines in social media posts, police investigators said.

— Eric Feigl-Ding (@DrEricDing) August 11, 2021

They also pointed out that although there was just a one in 50,000 chance of developing a clot after the vaccine, the chance of death was high.

Deadly blood clots have stopped since under-40s advised not to have AstraZeneca jab https://t.co/gBOZrpopcS

— Andrew Sweeney (@UH60Andy) August 11, 2021

The team found the average onset time was 14 days after the AstraZeneca jab, but said they had discovered no underlying factor that made people more susceptible. They also pointed out that although there was just a one in 50,000 chance of developing a clot after the vaccine, the chance of death was high.
Nearly one in four – 23 per cent – of those admitted with the condition in Britain died, many of whom were young and healthy. For those who developed a clot in the brain or other organs and had low blood counts, the risk of death rose to 80 per cent.

The numbers of cases and follow-up periods are currently insufficient for the estimation of vaccine effectiveness against severe disease, including hospitalization and death.